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ÖgeDetermining an organizational structure model toimprove productivity in architectural design offices(Graduate School, 2025-05-26)While organizational productivity and its influencing factors have been widely explored in the literature across various sectors, there remains a significant shortage of research dedicated specifically to architectural design offices (ADOs). ADOs exhibit distinctive operational characteristics—such as their reliance on project-based workflows, interdisciplinary teamwork, and creativity-centered outputs—that set them apart from more conventional corporate or industrial settings. Despite these unique aspects, the organizational management of ADOs has been largely overlooked in productivity-focused research. This gap may stem from the limited incorporation of architectural practice management into the broader discourse of organizational theory. Consequently, there is a notable deficiency in our understanding of how productivity can be accurately measured, evaluated, and improved within ADOs, underlining the need for focused investigation in this specialized area. This study aims to determine an organizational structure model to improve organizational productivity in ADOs. To achieve this, the research began with a comprehensive review of relevant literature. This review encompassed a wide spectrum of topics including the foundational definitions and historical origins of organizations, their components, and particularly, the attributes of project-based organizations. Emphasis was placed on understanding the implications of organizational structure—its role, typologies, and theoretical foundations—with particular focus on aligning structural design to organizational objectives. Additionally, the literature examined the concept of organization on the individual level, addressing personal and professional self-organization and its impact on workplace productivity. The review also investigated the connection between organizational structure and performance, providing a framework for defining and evaluating productivity within organizational contexts. It identified various productivity-enhancing strategies and outlined how performance can be effectively assessed. Furthermore, the study narrowed its focus to ADOs, exploring their internal structures, workflows, and the role of architectural management (AM) in coordinating both creative and technical tasks. It was observed that existing research on factors influencing productivity in ADOs remains scarce and lacks empirical depth. Based on this theoretical groundwork, the study proposed the following hypothesis: Organizational Structure with 7 Divisions (with functionel structure subcategories) is a suitable model to improve organizational productivity in ADOs. This model was chosen for its comprehensiveness and product-oriented design, which is thought to correspond well with the project-based nature of ADOs. As design offices typically operate with distinct, time-bound projects that mirror the characteristics of projectbased organizations (PBOs), a structure built around such logic is deemed appropriate and potentially beneficial. To test this hypothesis, a quantitative research methodology was employed. A survey was distributed among professionals working in ADOs to evaluate their organizational awareness, the degree to which functions of the proposed model are currently applied, and the perceived impact of these functions on productivity. The survey data were analyzed using a range of statistical techniques, including reliability analysis, normality testing, correlation analysis, and independent samples t-tests. The findings revealed that more than half of the participants lacked awareness of a clearly defined organizational structure within their offices, suggesting a general absence of organizational clarity. However, it was also observed that many of the functions associated with the proposed structure were already being partially implemented. The data shows a notable mismatch between how some functions are used and how important they are thought to be. Despite being underutilized, "strategic planning for organizational growth" was found to have the greatest influence on productivity, underscoring the necessity for ADO owners and employees to understand the significance of strategic planning. Financial operations like "expense management" and "revenue management" were widely employed and thought to be very productive. On the other hand, "keeping and archiving customer information" came in last in terms of perceived productivity impact yet somewhat high in utilization (i.e., it cannot be argued that it is utilized less), indicating that its high use may be due to factors other than productivity. As a general conclusion, it can be said that the analysis supported the hypothesis, indicating that the suggested model holds significant potential for improving productivity in ADOs.
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ÖgeExploring the potential of digital twin technology to improve factors affecting construction productivity during the construction phase(Graduate School, 2025-05-26)In the construction industry, productivity has been a challenging and significant problem. Despite its significant economic importance, the construction sector often underperforms due to inefficiencies observed across various project stages. Low productivity is caused by a variety of problems, including insufficient communication, labor shortages, inadequate planning and a limited integration of digital technologies. These serious problems eventually decrease the general efficiency and sustainability of construction projects by causing delays, cost overruns, risk factors and resource waste, particularly during the building stage. As a result of these problems the construction sector is becoming more interested in advanced technologies in order to experience a digital transformation. Thus, the industry may achieve better site control, more effective decision making, and improved collaboration. Digital twin (DT) technology is one of these innovations that is currently gaining attention as an exciting concept that could help with major inefficiencies during the building phase. DT makes it possible for virtual and physical environments to synchronize in current time while offering insights based on data for performance enhancement. When integrated with Construction 4.0 technologies such as internet of things (IoT), artificial intelligence (AI), machine learning (ML) and others, DT systems can enable predictive maintenance, better visualization, dynamic planning and modelling construction processes. According to current literature, the DT concept may be particularly beneficial during the building stage since the construction phase is characterized by complicated resource allocation, changing scheduling and critical cost, time and safety performance goals. DT implementation during this stage might facilitate real time site management, decrease rework, enhance safety conditions and allow for a more prepared decision making process. Despite these encouraging advantages, the implementation of DT in the construction stage remains limited, largely due to high costs, technical barriers and lack of awareness. This research investigates the potential of DT technology to improve productivity in the construction phase by examining how its capabilities align with the factors negatively affecting project performance. To achieve this goal, the objectives of the study are (1) to explore the role of DT throughout the building life cycle in the construction sector (2) to identify its benefits, challenges, and key application areas in the construction stage (3) to determine the factors affecting productivity during the construction phase (4) to compare these factors with the DT system's identified capabilities to assess its potential in overcoming productivity challenges. To accomplish these goals, the research adopts a two step method. First, a comprehensive literature review was conducted to identify the most critical productivity related factors and discover the present knowledge of DT system's role in construction such as its opportunities, obstacles and major application areas. As a second step, the questionnaire was designed based on the literature findings to understand the perception of sector experts and distributed to professionals in the construction industry. This resulted in 76 valid responses. The data were analyzed using SPSS v.29. The collected data were analyzed using several statistical methods including descriptive statistics, Cronbach's alpha reliability analysis, normality tests, correlation analysis, independent samples t-tests and one-way ANOVA. These methods enabled a detailed evaluation of relationships between variables and the identification of notable variations in perceptions across participant groups. Findings from the survey suggest that industry professionals recognize the high potential of DT technology to address key productivity challenges in the construction phase. According to the survey results, professionals identified labor, management systems and design related issues as the most influential and frequently occurring productivity factors. However, when assessing DT system's potential impact, participants believed the greatest improvements would occur in design related issues and management systems. These areas where DT capabilities such as integration with emerging technologies and time, cost optimizations are particularly effective. Despite labor being the most important productivity factor, the associated DT application area workforce monitoring was ranked lowest. This may indicate that DT was not believed to have much of an impact on labor concerns. In the statistical analyses, strong correlations for the benefits of DT concept were found between risk management, real time digital representation and resource management, as well as between time and cost management and resource management. In the challenges of the DT category, the highest correlations appeared among data integration, data management and high-fidelity modeling. For key application areas of DT, strong links were observed between material and equipment management, site monitoring and time and cost optimization. In addition to descriptive and correlation analyses, the study conducted independent samples t-tests and one-way ANOVA to explore differences in perceptions among participant groups. According to t-tests and ANOVA, there were minor variations between the participant groups. For the t-test, participants were divided into two groups based on their prior knowledge of DT technology (DT-informed and uninformed). Although no significant differences were observed in perceptions of respondents on the productivity factors and the opportunities of the DT system, significant differences emerged in specific areas. Regarding the potential impact of DT on productivity related factors, a statistically significant difference was identified for the communication factor, suggesting that DT-informed participants believed DT implementation could more strongly improve communication during the construction phase. In the challenges, DT-informed participants rated obstacles such as data integration, high-fidelity modeling and the need for skill and training more critically than non-informed participants. Similarly in the key application areas of DT in the construction phase, a significant difference was observed for the enhanced decision making processes variable, where DT-informed participants rated this application area more highly than non-informed participants. For the one-way ANOVA, participants were categorized according to their professional experience: less than 2 years, 2–5 years and more than 5 years. Even though opinions were generally the same among the groups, two notable distinctions were found. First, participants with more than 5 years of experience reported a higher mean score for DT's adaptability performance compared to those with 2–5 years of experience. Second, in the integration with the emerging technologies application area, those with more than 5 years of experience again rated the application area significantly higher compared to participants with 2–5 years of experience. These findings may imply that more experienced professionals tend to perceive a more positive view of DT as more adaptable and better suited for integration with advanced technologies in construction processes. According to the results obtained from the study, industry experts are optimistic about the DT technology and believe that it can significantly influence the factors determining efficiency and positively affect the productivity in the construction phase.
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ÖgeComparison of the architectural design process quality between BIM and traditional design methods(Graduate School, 2022)Quality in the construction industry requires meeting the requirements and needs of the designers, engineers, and contractors involved in the process, as well as satisfying the customer's expectations. Although quality standards and quality improvement policies are not as established as in the manufacturing industries, it is important for organizations in the construction industry to gain a competitive advantage by ensuring high quality and low costs. When considering construction quality, quality control of the productions and resources that take place during construction usually comes to mind. However, this approach does not include investigating and improving the root causes of quality problems arising from the planning and design processes. In addition, the fact that the control and improvement of quality problems that arise in construction projects are practiced during the construction phase prevents the quality problems derived from design and engineering errors to be solved at the design stage with lower costs and resources. There are also instances where a design defect cannot be corrected in construction at reasonable costs. For this reason, the understanding of quality in construction should be approached in two ways. First of all, product quality in construction is related to the effectiveness of materials, equipment and technologies used in the construction phase. Secondly, process quality in the construction industry is related to the organization and management methods applied during the design, construction and operation phases. The expectations of the stakeholders (i.e., building owner, designers, engineers and management and organization) involved in the architectural design process are different from each other, and therefore they consider different factors when evaluating the design process quality. In order for all stakeholders to develop a common understanding of quality, effective communication methods between stakeholders should be determined, feedback systems should be established, and all parties should be involved in the process from the very beginning of the project design. As a result, the design output, which is expressed in clear and effective project documentation, developed by receiving feedbacks and checked for constructability, ensures that the product reaches the expected quality. In traditional design methods, the design process is divided into sub-processes and the complex design process is tried to be facilitated. However, this division causes the design process to progress independently between disciplines and causes separation between design teams, creating problems in communication, collaboration and integration, which are very important for achieving design quality. Therefore, the adaptation of digitalization, integration and collaboration tools in the design process has the potential to provide solutions to the quality problems present in the design processes. The Building Information Modeling (BIM) method, which has been discussed since the 2000s to provide solutions to these problems, can be used as a tool to improve the quality of the architectural design process with its features such as involving all stakeholders in the design process, providing interdisciplinary integration and supporting information sharing. In this study, the effect of BIM adaptation on the factors affecting quality in the architectural design process is compared with traditional design processes, to conclude whether the use of BIM is a suitable method to improve the quality of the architectural design process. Within the scope of the study, it was decided to conduct a survey as a research method, with the anticipation that there will be a difference between the evaluation of the design process quality of the architects who use traditional design processes and BIM processes in current architectural design applications. First, by conducting a comprehensive literature research, architectural design process is defined in a structured manner and the differences between traditional and integrated design processes were determined. Then, BIM process was examined; the usage areas, maturity levels and benefits of BIM to the design process were explained. Finally, the factors that determine the quality in the design process were determined and the quality evaluation scales for the survey study were prepared by comparing these factors with the benefits of BIM. In order to evaluate the difference between traditional architectural design processes and BIM processes in the context of quality management concepts, these scales were determined as: (1) design requirements, (2) communication (3) drawing and specification control, (4) tools, methods and techniques, (5 ) design validation, (6) project team, and (7) management and organization. In the analysis of the data obtained from a total of 83 participants as a result of the survey conducted with BIM and traditional design process users, the focus was on the participants' evaluation of the quality factors in the architectural design processes and the relationship of these factors with the design process quality. The data obtained through the survey from the evaluations of 42 users of BIM and 41 traditional design processes, were analyzed with descriptive and statistical analysis methods. Before the analysis of the data, reliability analysis was performed on the scales and it was determined that the scales used in the questionnaire were reliable. To determine the relationships between the scales the correlation analysis is applied, the relationships between the factors affecting the quality of the architectural design process of the participants presents conformity with the findings of the literature review. For the comparison of the participant groups, descriptive analysis was first applied, the mean values of the evaluations were presented, and it was observed that there was a difference in the evaluation of the quality scales between the two groups. In order to determine the statistically significant relationships between these differences, t-test analysis was performed for independent samples. The results of the independent samples t-test analysis have presented statistical significance, which demonstrates that the architectural design process quality of the participants using BIM processes is higher than the traditional design processes. In particular, significant differences were seen between the two groups in the scales of tools, methods and techniques (μd = 2,14), design verification (μd = 1,97), drawing and specification control (μd = 1,91), communication (μd = 1,85), management and organization (μd = 1,20), and design requirements(μd = 1,15). It was observed that this difference was less effective at the scale of the project team (μd = 0,47). Within the scope of this study, these results indicate that the use of BIM improves the quality of the architectural design process.
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Ögeİnşaat sektöründe çalışanların bakış açısından yapım projelerinde bilgi israfı(Lisansüstü Eğitim Enstitüsü, 2023)İnşaat sektörü örtülü bilginin yoğun olarak üretildiği bir sektördür. Örtülü bilginin aktarılması, paylaşılması veya depolanması zordur. Bir firma, örtülü bilgiyi herkesin anlayabileceği ve erişebileceği hale, yani açık bilgiye dönüştürdüğü zaman bilgi, firma içerisinde tekrar kullanılabilecek ve organizasyon için stratejik değere dönüşecektir, dolayısıyla kaybolmayacaktır. İnşaat projelerinin çok paydaşlı olması, her projede farklı uzmanlık alanlarından ekiplerin yer alması, ekiplerin sürekli değişmesi ve üretim sürecinin uzun olması gibi nedenlerden ötürü örtülü bilginin açık bilgiye dönüşümünde bazı aksaklıklar yaşanabilmektedir. Firmalar sahip oldukları entelektüel sermayeden yararlanma konusunda başarısız olmakta ve çalışanların sahip oldukları örtülü bilginin firmaya değer üretecek şekle sokulması güçleşebilmektedir. Bu durum firmanın bilgi israfı riskiyle karşı karşıya kalması demektir. Bilgi israfı, organizasyon içerisinde erişilebilir düzeyde olsa da, değer üretmek veya müşterinin ihtiyaçlarını karşılamak için kullanılmayan bilgiyi ifade etmektedir. Çalışan ve çalışanın sahip olduğu bilgi hala örgüt içerisindedir fakat örgütsel sistemde sahip oldukları bilginin keşfedilmesine, kullanılmasına ve uygulanmasına izin vermeyen sorunlar veya verimsizliklerle karşılaşılmıştır. Literatürde bu verimsizliklere, bilginin örtülü bilgiden açık bilgiye dönüşümünde ve bilgi yönetimi sürecinde yaşanan aksaklıklar örnek gösterilmektedir. Bu tez çalışmasının amacı inşaat sektöründe bilgi israfının oluşumuna sebep olan durumları çözümlemek, bu israf türüne dair farkındalık düzeyini tespit etmek, inşaat firmalarında bilgi israfının yönetimine veya önlenmesine dair uygulamaları, eksiklikleri, ihtiyaçları ve önerileri belirlemektir. Toplamda altı bölümden oluşan tezin birinci bölümünde literatür özetiyle çalışmaya giriş yapılmış, çalışmanın amacı ve yöntemi açıklanmıştır. İkinci bölümde çalışmanın kuramsal çerçevesini oluşturan literatür taraması verilmiştir. Üçüncü bölümde literatürde güncel bir çalışma alanı olan ve bilgi israfı kavramının arka planını oluşturan bilgi riskleri konusu ele alınmış ve literatürde bahsi geçen bilgi israfı çeşitlerine detaylarıyla birlikte yer verilmiştir. Tezin beşinci bölümünde saha çalışmasında elde edilen bulgular çizelge ve şekillerle sunulmuş, yorumlanmış ve tartışmaya açılmıştır. Altıncı bölümde çalışmanın özetine ve inşaat firmalarının bilgi israfının önlenmesinde uygulayabileceği yöntemlere dair önerilere yer verilmiştir.
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ÖgeArtificial intelligence influence for digitalized construction project management during planning phase(Graduate School, 2024-11-12)Digitalization has become mandatory considering the efficiency and productivity criteria reached in the 21st century and the developments in the construction industry in the last decade. Considering the size and employment rate it occupies in the world economy, the revolutionary digitalization adventure, which continues but has a long way to go, is far from being completed. The most valuable potential of Artificial Intelligence (AI) is that all industry components that play a role in the project life cycle can understand and benefit from it, even at different levels. AI is a set of sciences, theories and techniques whose purpose is to reproduce the complex tasks that a human can perform and the cognitive abilities of a human done by a machine. Additionally, these systems can process data and information like intelligent behavior, often including reasoning, learning, perception, prediction, planning or control elements. However, like many technologies in the construction industry, where the digital revolution is not completed, AI has not yet been fully adopted. In the construction industry, which differs negatively from other industries by starting digitalization late, digitalized practices can be observed at every stage of the project life cycle. Still, they cannot be significantly differentiated at any stage. However, various studies have noticed the greatest potential hidden in the planning phase. For this reason, this thesis aims to convey valuable information about AI to the sector components, shed light on the digital project and construction management planning phase from AI's perspective, contribute to its adaptation. (1) "What are the characteristics of Artificial Intelligence technologies in the construction industry?", (2) "What are the functions and applications of Artificial Intelligence in the construction industry?", (3) "What are the key criteria to be considered for the performance and impact of adopting Artificial Intelligence?", ( 4) "To what extent and how can Artificial Intelligence, subsets and technologies support the management of the construction project planning phase?"; Answers to these questions were sought to achieve the aims of this research. When the project construction literature is examined comprehensively, although the potential of AI technologies in the project planning phase is revealed, a lack of information and research on digital adaptation is noticed. Research conducted in the search engines of various databases focused on studies on "artificial intelligence features and subfields", "artificial intelligence applications", "relationship between artificial intelligence and the construction industry", and "artificial intelligence applications in the planning stage". As a result of the literature review, six major artificial intelligence-supported services that have the potential to play an active role in digitalized project planning in the construction industry were identified. These influence areas of AI are automated project scheduling, labor/productivity management, predictive modeling and risk determination, health and safety, accessible data and cost engineering. In the next step, a survey was conducted to various professionals from the construction industry to investigate the impact of using AI for digital construction project management during the planning phase. It was aimed to reach the effects of the concept of AI on project and construction management at the planning phase with the answers received to the questions asked on a 7-point Likert scale (1=not effective, 7=extremely effective) in the first part of the survey, which consists of two parts. In the second part, questions were asked to obtain demographic information about the survey participants. The aim of the survey study was to reach a 50% response rate, which was planned to be shared with 120 participants from the construction industry, determined by purposive sampling. The data obtained from the survey conducted using the SPSS 27 program was analyzed by subjecting it to various tests. First, Cronbach's alpha (α) reliability test was applied due to the need to test data reliability. Then, which of the various parametric tests would be used in the advanced statistical analyses planned to be carried out within the scope of the study is evaluated depending on the normality values of the Skewness-Kurtosis test. Later, the obtained data were subjected to the Pearson correlation test, which revealed the relationships between the evaluation criteria presented to the participants in the first part of the survey. Then, depending on the number of groups, independent samples t-test and ANOVA test were applied to evaluate the variables of the relevant groups, in order to search whether there is statistically significant difference in this evaluation. The t-test was applied to two paired groups. These groups are designed as (1) professions, (2) users and non-users of AI applications. During the one-way ANOVA test, the results divided into more than two groups were analyzed whether there is a significant difference in variable mean values. ANOVA groups are designed as (1) experience in the AECO industry, (2) associated organization, (3) organization scale and (4) years of experience with AI and concepts. Finally, Levene and post-hoc tests were applied to complete this phase of study. In scaling the effect of the specified AI applications on the variables, all influence areas have high mean scores. In the planning phase of construction projects, the highest effect was observed in accessible data, while the lowest was observed in health and safety. It was also understood that many influence areas were highly correlated. To sum up, within the scope of this thesis study, the concept of AI, its features, and current and future applications among digital technologies in the construction industry have been examined and analyzed. It aims to contribute to the limited literature, especially considering its position in the planning phase from the project and construction management perspective. In addition, many AI tools, applications and contributions that can be used during the construction project management and planning phase are mentioned. Also, the results and potentials that will arise from its practical use are mentioned. In this way, it can be claimed that the use of AI for digitalized project planning has the potential to solve low productivity and inefficiency problems, the biggest problems that the construction industry has had difficulty overcoming for decades. Finally, it is intended that this completed study will be a source of inspiration for industry-component companies, institutions, individuals and especially researchers who need motivation for the digitalization and progress of the construction industry.